Which grain transportation mode is 'greenest'?

by C. Phillip Baumel
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In the October 2009 issue of World Grain, the article "Grain Transportation’s Green Alternative" (GTGA) proposed increased water transportation of grain as the "green alternative" to trucks and railroads. The suggested purposes were to alleviate congestion on highways and railroads and reduce the carbon footprint of transportation. Reducing the carbon footprint implies reducing the total consumption of fuel. GTGA uses inland waterway barges as a proxy for all water transportation and offers the following net-ton miles of freight moved per gallon of fuel (NTMG) as measures of the fuel efficiency:

Mode ......................................................……...NTMG

Truck ................................................................155

Rail...................................................................413

Barge ...............................................................576

These NTMG suggest that barges are 39% more fuel efficient than railroads. However, NTMG alone tell only part of the fuel efficiency story. Almost all grain shipped by barge must be hauled from inland elevators or from farms to barge-loading elevators. Grain shipments from elevators direct to a final destination are typically in rail or truck. Usually, no other mode is involved in these transfers. To make correct comparisons of total fuel consumption of barge versus rail direct to an export port, fuel consumed by truck or rail to a barge elevator must be added to the barge fuel consumption. Moreover, NTMG measures only the miles that one ton of freight is moved by one gallon of fuel. It fails to measure the total fuel consumed in moving the freight from an origin to a destination.

Table 1 (page 50) shows the total fuel consumed to move one ton of grain from one origin to one destination by rail and truck-barge. The NTMG listed in Table 1 are those suggested in GTGA. The Association of American Railroads (AAR) reports that the average NTMG for Class I railroads for 2009 was 480; this is 16% more fuel efficient than the 413 reported in GTGA. Nevertheless, Table 1 uses GTGA’s lower rail NTMG of 413.

Waterloo, Iowa, U.S., almost in the center of Black Hawk County, was chosen as the origin of the grain in Table 1. St. Charles Parish, Louisiana (SCPL) was selected as the destination for both the rail and barge delivered grain. Three of the 10 grain export elevators in the New Orleans, Louisiana, U.S. (NOLA) area are located in SCPL. The rail miles from Waterloo to SCPL were obtained from the AAR. They are the average miles for typical routes of rail shipments of grain from Black Hawk County to SCPL.

Dubuque, Iowa, U.S., directly east of Waterloo, was chosen as the location of the barge-loading elevators. The truck miles from Waterloo to Dubuque were taken from MapQuest. The barge miles from Dubuque to SCPL were obtained from the U.S. Army Corps of Engineers and the Iowa Department of Transportation.

The data in Table 1 show that total fuel consumption per ton of grain shipped from Waterloo to SCPL is 6% greater for truck-barge than for rail direct. If the AAR 2009 rail NTMG of 480 was substituted for the GTGA rail NTMG in Table 1, the total truck-barge fuel consumption would be 24% greater than for direct rail. These results are the opposite of the conclusion derived from NTMG alone. The major reasons for the different conclusions are:

1. The truck portion of the barge movement adds almost 0.6 gallons to total truck-barge fuel consumption per ton of grain, and

2. The total barge distance from Dubuque to SCPL is 20% longer than the average rail distance from Waterloo to SCPL.

The longer barge distance is caused by the meandering of the Mississippi River. The Blue Water Shipping Company map (see page 51) illustrates the impact of the meandering of the river on river distances. All of the NOLA grain export elevators are located within the Baton Rouge-Myrtle Grove section of the river. The river distance between these two points is 167 miles. The Map-Quest driving distance between these two points is 107 miles. Thus, the meandering of the river increases the river distance between these two points 56% above the driving distance.

The total miles to an importing country is even more important for calculating total fuel consumption for grain destined for export. For example, corn (maize) exports to Japan typically move in two directions. One is by barge, rail or truck to Gulf of Mexico ports (including NOLA) for ocean vessel movements through the Panama Canal. The second is by rail to the West Coast and ocean vessel to Japan. The rail movement from Iowa to the West Coast is longer than to NOLA and it is over the Rocky Mountains. This suggests that fuel consumption would decrease if Iowa corn was shipped to NOLA ports for export. However, the ocean distance from NOLA to Japan is more than double the distance from Seattle (almost 6,000 miles longer). The net result is that corn shipped by rail from Iowa to Seattle and ocean vessel to Japan uses less total fuel than any modal combination through NOLA.

Most estimated NTMG are averages over all movements for a given mode. Barge NTMG is typically calculated by dividing total net-tons of freight hauled by all barges on all navigable rivers by the total number of gallons of fuel consumed. The Lower Mississippi River — that portion of the river south of the confluence of the Ohio and Mississippi rivers — is the most fuel-efficient river on the Mississippi River system. NTMG increases sharply as the number of tons increases in barge tows. Barge tows on the Lower Mississippi can have 50 or more barges. That compares with a maximum of 15 barges on the Upper Mississippi River and as few as two on the Missouri River. Second, the river current on the Lower Mississippi is swift because it is not impeded by dams. The swift current pushes the loaded barges downstream, further reducing fuel consumption. Third, barge tows move nonstop on the Lower Mississippi River. Barge tows on most other rivers must stop to transit the locks at each dam. This suggests that barge NTMG should be calculated for individual rivers to generate more accurate NTMG estimates.

Similar issues exist for railroads. The large tonnages and direct shipments of unit-trains make them more fuel efficient than manifest trains. Trains that cross mountains consume more fuel than trains that essentially follow, but don’t meander, along the Mississippi River.

Finally, new technology locomotives, like alternating current locomotives, are highly fuel efficient. This suggests that NTMG should be estimated for different types of rail service.

CONCLUSIONS

The conclusions that can be drawn from this information are:

1. NTMG, when used alone, is an incomplete and misleading measure for modal fuel efficiency comparisons.

2. A more accurate measure of competitive modal fuel efficiency is the total fuel consumed by each mode in the transfer of the grain from the same origin to the same final destination. This measure can be calculated by dividing the total miles traveled by each mode by the appropriate NTMG for each mode. Fuel consumption by each mode should be summed to obtain the total fuel consumption for multimodal shipments.

3. Appropriate NTMG should be estimated for different rivers and for different sizes and types of trains traveling over different terrains and with different types of locomotives.

4. There is no one "greenest" mode of freight transport. The greenest mode or group of modes depends on several variables. These include the origin, final destination, accuracy of the NTMG and the miles traveled by all modes involved in the transfer.

5. Government officials should carefully examine proposals for public infrastructure investments that use NTMG alone to justify the proposal’s fuel efficiency. These proposals may have the unintended consequence of increasing total fuel consumption. Accurate proposals would provide estimates of total fuel consumption from the beginning origin to the final destination. They should be based on appropriate NTMG measures and total miles traveled by each mode involved in the transfer. These estimates should be compared with the next best alternative movement. Table 1 is an example of that type of comparison.

C. Phillip Baumel is a Distinguished Professor Emeritus of Economics at Iowa State University.

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